SC data entry: Packets 1-3

KW initial exploration (updated with data from 2018-01-31)

This is a document describing Kara’s initial exploration of the data from Packets 1-3.

These packets included 29 scales or subscales, including the following:

• Absorption (Tellegen); range: 0 to 34
• Absorption extra (a handful of extra questions extra questions); range: 0 to 4
• Daily spiritual experiences #1-4 (Underwood & Teresi); range: 0 to 20
• Daily spiritual experiences #5-14 (Underwood & Teresi); range: 0 to 50
• Daily spiritual experiences #5-14 (Thai) (additional questions asked only in Thailand); range: 0 to 60
• Daily spiritual experiences #15-16 (Underwood & Teresi); range: 0 to 10
• Daily spiritual experiences #15-16 (Thai) (additional questions asked only in Thailand); range: 0 to 30
• Spiritual events (Luhrmann); range: 0 to 88
• Sensory seeking (Brown et al.); range: -28 to 28
• Body awareness (Shields et al.); range: -36 to 36
• Attention to feelings (Salovey et al.); range: -42 to 42
• Hallucination (Alderson-Day); range: 0 to 27
• VISQ: dialogic speech (McCarthy-Jones & Fernyhough); range: -8 to 8
• VISQ: inner speech (McCarthy-Jones & Fernyhough); range: -10 to 10
• VISQ: evaluative/motivational speech (McCarthy-Jones & Fernyhough); range: -8 to 8
• Inner speech (Hardy/Bentall); range: -20 to 20
• Hearing events (Posey & Losch); range: 0 to 18
• Encoding style (Lewicki); range: 0 to 40
• Mind metaphors (Van Elk); range: -16 to 16
• TAT: lack of cognitive confidence (Wells et al.); range: -12 to 12
• TAT: positive beliefs re: worrying (Wells et al.); range: -12 to 12
• TAT: cognitive self-consciousness (Wells et al.); range: -12 to 12
• TAT: uncontrollability/danger (Wells et al.); range: -12 to 12
• TAT: need to control thoughts (Wells et al.); range: -12 to 12
• Dualism: mental states (Weisman); range: 0 to 8
• Dualism: life events (Weisman); range: 0 to 5
• Dualism: inanimate consciousness (Weisman); range: 0 to 6
• Dualism: minds, selves, & world (Weisman); range: 0 to 9
• Dualism: epistemology (Weisman); range: 0 to 5

Means by site

First, let’s compare the mean responses to each subscale across the 5 sites. I’ll plot each subscale in a separate mini-plot (denoted in the title of each mini-plot). The x-axis and color denote the site (US, Ghana, Thailand, China, or Vanuatu). Note the the range y-axis varies between mini-plots. The dot shows what the mean response was for that subscale for that site, and the error bars show a 95% confidence interval for that mean.

Joining, by = c("ctry", "packet", "subscale")

Cluster analysis

… of sites

Now let’s try a formal analysis for determining how similar these 5 sites are to each other.

Hierarchical clustering basically works like this: If there are 5 things - A, B, C, D, and E - this analysis will try to group pairs of like things together. E.g., first it might pair A and D together, then it will pretend that there are just 4 things - AD, B, C, and E. Then it might pair B and C together, and then pretend that there are just 3 things - AD, BC, and E. Then it might pair AD and BC together, and then pretend that there are just 2 things: ADBC and E. It will do this until there is just one “thing” left.

So to read this plot, look for the pairings. Sites that are on the same “branch” in this “dendrogram” showed similar patterns of means on the subscales. The closer together they are on that branch, the more similar they were.

… of subscales

Now let’s do the same thing for the subscales: Which subscales “hang together”?

To read this plot, again, look for the pairings. Subscales that are on the same “branch” in this “dendrogram” showed similar patterns of means across the 5 sites. The closer together they are on that branch, the more similar they were.

Look at correlations among subscales by site

Now let’s take a closer look at which subscales seem to “hang together,” by looking at the correlations between scales in their means for each site.

To read this plot, find one scale on the x-axis, and another scale on the y-axis. The value (and color) at that point in the grid shows you the correlation between these two subscale means across the 5 sites. The maximum possible correlation is +1 (red), and the minimum is -1 (blue).

I ordered the scales according to the cluster analysis we just did above, so you can see “patches” of scales that all tended to have similar patterns of means across sites.

Look at correlations among subscales by individuals

We could do the same thing thinking about individual participants instead of sites - but it’s important to keep in mind that most people didn’t fill out all 29 subscales! So we’ll focus on just looking at how the subscales within each packet (Packet 1, 2, or 3) “hang together” for the people who completed that packet.

As above, to read these plots, find one scale on the x-axis, and another scale on the y-axis. The value (and color) at that point in the grid shows you the correlation between these two subscale means across the 5 sites. The maximum possible correlation is +1 (red), and the minimum is -1 (blue). (Note that these scales are being presented in alphabetical order, not by any sort of clustering analysis.)

Packet 1

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Packet 2

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Packet 3

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All packets

Just for fun, here’s a look at the correlations among individual participants’ subscale scores across all scales. Note that some of these pairs of subscales probably have very few observations going into these correlations!! So take this with a grain of salt.

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